{"id":"https://openalex.org/W2021995444","doi":"https://doi.org/10.1109/icip.2013.6738646","title":"A hypergraph based semi-supervised band selection method for hyperspectral image classification","display_name":"A hypergraph based semi-supervised band selection method for hyperspectral image classification","publication_year":2013,"publication_date":"2013-09-01","ids":{"openalex":"https://openalex.org/W2021995444","doi":"https://doi.org/10.1109/icip.2013.6738646","mag":"2021995444"},"language":"en","primary_location":{"id":"doi:10.1109/icip.2013.6738646","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2013.6738646","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2013 IEEE International Conference on Image Processing","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5038141674","display_name":"Zhouxiao Guo","orcid":null},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhouxiao Guo","raw_affiliation_strings":["School of Science and Engineering, Beihang University, Haidian District, Beijing, China","[School of Science & Engineering, Beihang University, Beijing, China]"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Science and Engineering, Beihang University, Haidian District, Beijing, China","institution_ids":["https://openalex.org/I82880672"]},{"raw_affiliation_string":"[School of Science & Engineering, Beihang University, Beijing, China]","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Xiao Bai","orcid":null},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiao Bai","raw_affiliation_strings":["School of Science and Engineering, Beihang University, Haidian District, Beijing, China","[School of Science & Engineering, Beihang University, Beijing, China]"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Science and Engineering, Beihang University, Haidian District, Beijing, China","institution_ids":["https://openalex.org/I82880672"]},{"raw_affiliation_string":"[School of Science & Engineering, Beihang University, Beijing, China]","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100419458","display_name":"Zhihong Zhang","orcid":"https://orcid.org/0000-0002-0542-0640"},"institutions":[{"id":"https://openalex.org/I191208505","display_name":"Xiamen University","ror":"https://ror.org/00mcjh785","country_code":"CN","type":"education","lineage":["https://openalex.org/I191208505"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhihong Zhang","raw_affiliation_strings":["Xiamen University, Siming District, Xiamen, China","Xiamen University, Xiamen, CHINA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Xiamen University, Siming District, Xiamen, China","institution_ids":["https://openalex.org/I191208505"]},{"raw_affiliation_string":"Xiamen University, Xiamen, CHINA","institution_ids":["https://openalex.org/I191208505"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100781212","display_name":"Jun Zhou","orcid":"https://orcid.org/0000-0001-5822-8233"},"institutions":[{"id":"https://openalex.org/I11701301","display_name":"Griffith University","ror":"https://ror.org/02sc3r913","country_code":"AU","type":"education","lineage":["https://openalex.org/I11701301"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Jun Zhou","raw_affiliation_strings":["School of Information and Communication Technology, Griffith University, Nathan, QLD 4111, Australia","School of Information and Communication Technology, Griffith University, Nathan, Qld., Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Information and Communication Technology, Griffith University, Nathan, QLD 4111, Australia","institution_ids":["https://openalex.org/I11701301"]},{"raw_affiliation_string":"School of Information and Communication Technology, Griffith University, Nathan, Qld., Australia","institution_ids":["https://openalex.org/I11701301"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":26,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"3137","last_page":"3141"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11667","display_name":"Advanced Chemical Sensor Technologies","score":0.9832000136375427,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10640","display_name":"Spectroscopy and Chemometric Analyses","score":0.9793000221252441,"subfield":{"id":"https://openalex.org/subfields/1602","display_name":"Analytical Chemistry"},"field":{"id":"https://openalex.org/fields/16","display_name":"Chemistry"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.9410840272903442},{"id":"https://openalex.org/keywords/hypergraph","display_name":"Hypergraph","score":0.8799260854721069},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.7004653811454773},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6897178292274475},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6535025238990784},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6474149227142334},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.5519829392433167},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.41614121198654175},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.412203848361969},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.37793296575546265},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.21337202191352844}],"concepts":[{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.9410840272903442},{"id":"https://openalex.org/C2781221856","wikidata":"https://www.wikidata.org/wiki/Q840247","display_name":"Hypergraph","level":2,"score":0.8799260854721069},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.7004653811454773},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6897178292274475},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6535025238990784},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6474149227142334},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.5519829392433167},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.41614121198654175},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.412203848361969},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.37793296575546265},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.21337202191352844},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C118615104","wikidata":"https://www.wikidata.org/wiki/Q121416","display_name":"Discrete mathematics","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/icip.2013.6738646","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2013.6738646","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2013 IEEE International Conference on Image Processing","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.709.2238","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.709.2238","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.ict.griffith.edu.au/%7Ejunzhou/papers/C_ICIP_2013_C.pdf","raw_type":"text"},{"id":"pmh:oai:research-repository.griffith.edu.au:10072/57162","is_oa":false,"landing_page_url":"http://hdl.handle.net/10072/57162","pdf_url":null,"source":{"id":"https://openalex.org/S4306402548","display_name":"Griffith Research Online (Griffith University, Queensland, Australia)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I11701301","host_organization_name":"Griffith University","host_organization_lineage":["https://openalex.org/I11701301"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Conference output"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Decent work and economic growth","score":0.7799999713897705,"id":"https://metadata.un.org/sdg/8"}],"awards":[{"id":"https://openalex.org/G4969891065","display_name":null,"funder_award_id":"61105002","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W63231299","https://openalex.org/W1522099992","https://openalex.org/W1965680102","https://openalex.org/W1993022127","https://openalex.org/W2040589129","https://openalex.org/W2047029347","https://openalex.org/W2060542593","https://openalex.org/W2063978378","https://openalex.org/W2067869061","https://openalex.org/W2067983477","https://openalex.org/W2097900616","https://openalex.org/W2109488347","https://openalex.org/W2132709984","https://openalex.org/W2138038253","https://openalex.org/W2149440950","https://openalex.org/W2150990614","https://openalex.org/W2151660600","https://openalex.org/W2154506590","https://openalex.org/W2156932943","https://openalex.org/W2161943337","https://openalex.org/W2538885589","https://openalex.org/W3150214740","https://openalex.org/W6682105535"],"related_works":["https://openalex.org/W3211035526","https://openalex.org/W1869808405","https://openalex.org/W2018257962","https://openalex.org/W4291701050","https://openalex.org/W2028628118","https://openalex.org/W2565015337","https://openalex.org/W2891352623","https://openalex.org/W2775464024","https://openalex.org/W2972973180","https://openalex.org/W2082083895"],"abstract_inverted_index":{"Band":[0],"selection":[1,104],"is":[2,89],"a":[3,15,20],"fundamental":[4],"problem":[5],"in":[6,61],"hyperspectral":[7,48],"data":[8],"processing.":[9],"In":[10],"this":[11,36],"paper,":[12],"we":[13],"present":[14],"semi-supervised":[16,53],"learning":[17,54,63],"approach":[18,88],"and":[19,84,93],"hypergraph":[21,42],"model":[22,43,67],"to":[23,65],"select":[24],"useful":[25],"bands":[26],"based":[27],"on":[28,91],"few":[29],"labeled":[30,73],"object":[31],"information.":[32],"The":[33,86],"contributions":[34],"of":[35,81],"paper":[37],"are":[38],"two-fold.":[39],"Firstly,":[40],"the":[41,52,62],"captures":[44],"multiple":[45],"relationships":[46],"between":[47],"image":[49],"samples.":[50],"Secondly,":[51],"method":[55],"not":[56],"only":[57],"utilizes":[58],"unlabeled":[59],"samples":[60,74],"process":[64],"improve":[66],"performance,":[68],"but":[69],"also":[70],"requires":[71],"little":[72],"which":[75,96],"can":[76],"significantly":[77],"reduce":[78],"large":[79],"amount":[80],"human":[82],"labor":[83],"costs.":[85],"proposed":[87],"evaluated":[90],"AVIRIS":[92],"APHI":[94],"datasets,":[95],"demonstrate":[97],"its":[98],"advantages":[99],"over":[100],"several":[101],"other":[102],"band":[103],"methods.":[105]},"counts_by_year":[{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":2},{"year":2018,"cited_by_count":6},{"year":2017,"cited_by_count":1},{"year":2016,"cited_by_count":2},{"year":2015,"cited_by_count":1},{"year":2014,"cited_by_count":3}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
